semantic_segmentation/pyproject.toml at main · MIC-DKFZ/semantic_segmentation · GitHub

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[project]
name = "semantic_segmentation"
dynamic = ["version"]
description = "A modular and extensible framework for training and evaluating semantic segmentation models with PyTorch Lightning, supporting multiple architectures, datasets, losses, and data augmentation pipelines out of the box."
readme = "README.md"
license = {file = "LICENSE"}
authors = [
  {name = "Lars Krämer"},
  {email = "lars.kraemer@dkfz-heidelberg.de"},
]
classifiers = [
    "Development Status :: 2 - Pre-Alpha",
    "Intended Audience :: Developers",
    "License :: OSI Approved :: Apache Software License",
    "Operating System :: OS Independent",
    "Programming Language :: Python",
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3 :: Only",
    "Programming Language :: Python :: 3.10",
    "Programming Language :: Python :: 3.11",
    "Programming Language :: Python :: 3.12",
    "Programming Language :: Python :: 3.13",
    "Topic :: Scientific/Engineering :: Image Processing",
]
requires-python = ">=3.10"

dependencies = [
    "hydra-core",
    "hydra-colorlog",
    "omegaconf",
    "lightning",
    "numpy",
    "tensorboard",
    "torch",
    "torchmetrics",
    "torchvision",
    "albumentations",
    "vidata",
    "napari_data_inspection",
    "patchly",
    "shortuuid",
    "madgrad",
    "segmentation_models_pytorch @ git+https://github.com/qubvel-org/segmentation_models.pytorch.git@main",
    "pandas",
    "matplotlib",
]


[project.optional-dependencies]
testing = [
    "tox",
    "pytest",  # https://docs.pytest.org/en/latest/contents.html
    "pytest-cov",  # https://pytest-cov.readthedocs.io/en/latest/
]

[build-system]
requires = ["setuptools>=42.0.0", "wheel", "setuptools_scm"]
build-backend = "setuptools.build_meta"

[tool.setuptools]
include-package-data = true

[tool.setuptools.packages.find]
where = ["src"]

[tool.setuptools.package-data]
"*" = ["*.yaml"]


[project.urls]
Homepage = "https://github.com/MIC-DKFZ/semantic_segmentation"
Code = "https://github.com/MIC-DKFZ/semantic_segmentation"

[tool.setuptools_scm]
write_to = "src/semantic_segmentation/_version.py"
fallback_version = "0.0.1+nogit"

[tool.coverage.run]
omit = [
    "*/__init__.py",
    "src/semantic_segmentation/datasets/preprocessing/*",
    "src/semantic_segmentation/playground/*",
]

[tool.coverage.report]
omit = [
    "*/__init__.py",
    "src/semantic_segmentation/datasets/preprocessing/*",
    "src/semantic_segmentation/playground/*",
]

[tool.pytest.ini_options]
addopts = "--cov=src/semantic_segmentation --cov-report=term-missing --cov-fail-under=70"
norecursedirs = ["src/semantic_segmentation/playground"]
markers = [
    "integration: marks tests that require external datasets or heavyweight setup",
]

[tool.black]
line-length = 100
target-version = ['py310', 'py311', 'py312', 'py313']

[tool.ruff]
line-length = 100
lint.select = [
    "E", "F", "W", #flake8
    "UP", # pyupgrade
    "I", # isort
    "BLE", # flake8-blind-exception
    "B", # flake8-bugbear
    "A", # flake8-builtins
    "C4", # flake8-comprehensions
    "ISC", # flake8-implicit-str-concat
    "G", # flake8-logging-format
    "PIE", # flake8-pie
    "SIM", # flake8-simplify
]
lint.ignore = [
    "E501", # line too long. let black handle this
    "UP006", "UP007", # type annotation. As using magicgui require runtime type annotation then we disable this.
    "G004"
]

exclude = [
    ".bzr",
    ".direnv",
    ".eggs",
    ".git",
    ".mypy_cache",
    ".pants.d",
    ".ruff_cache",
    ".svn",
    ".tox",
    ".venv",
    "__pypackages__",
    "_build",
    "buck-out",
    "build",
    "dist",
    "node_modules",
    "venv",
    "*vendored*",
    "*_vendor*",
]

target-version = "py310"
fix = true